Unsolicited bulk e-mail (UBE) or spam constitutes a significantfraction of all e-mail connection attempts and routinely frustratesusers, consumes resources, and serves as an infection vector formalicious software. In an effort to scalably and effectively reducethe impact of these e-mails, e-mail system designers have increasinglyturned to blacklisting. Blacklisting (blackholing, block listing) is aform of course-grained, reputation-based, dynamic policy enforcementin which real-time feeds of spam sending hosts are sent to networks sothat the e-mail from these hosts may be rejected. Unfortunately,current spam blacklist services are highly inaccurate and exhibitboth false positives and significant false negatives. In this paper, weexplore the root causes of blacklist inaccuracy and show that thetrend toward stealthier spam exacerbates the existing tension betweenfalse positives and false negatives when assigning spamming IPreputation. We argue that to relieve this tension, global aggregationand reputation assignment should be replaced with local aggregationand reputation assignment, utilizing preexisting global spamcollection, with the addition of local usage, policy, and reachabilityinformation. We propose two specific techniques based on this premise,\emph{dynamic thresholding} and \emph{speculative aggregation}, whosegoal is to improve the accuracy of blacklist generation. Weevaluate the performance and accuracy of these solutions in thecontext of our own deployment consisting of 2.5 million productione-mails and 14 million e-mails from spamtraps deployed in 11 domainsover a month-long period. We show that the proposed approachessignificantly improve the false positive and false negative rates whencompared to existing approaches.